ZED Raport z analizy danych
Marcin Burczyk
08 grudzień 2018
Podsumowanie analizy
Wykorzystane biblioteki
library(dplyr)
library(ggplot2)
library(ggforce)
library(gganimate)
library(tidyr)
library(caret)
library(DT)
library(summarytools)
library(gifski)
library(png)Dane
Powtarzalność wyników
Aby zapewnić powtarzalność wyników ustawiamy stan losowego generatora liczb.
set.seed(123)Wczytywanie danych
Dane rozdzielone są znakiem “;”, dlatego użyję funkcji read.csv2 do wczytania danych. Kolumna title jest połączeniem kolumn pdb_code, res_name, res_id oraz chain_id, zatem możemy ją usunąć podczas wczytywania, aby uniknąć powtarzania informacji.
all_data <- read.csv2("all_summary.csv", nrows = 1000, header = TRUE, dec=".", stringsAsFactors = FALSE) %>% select(-(blob_coverage:title))Wstępne
Wiersze, które w kolumnie “res_name” zawierają niepożądaną przez nas wartość zostają usunięte.
cleaned_data <- all_data %>% filter(!res_name %in% c('UNK', 'UNX', 'UNL', 'DUM', 'N', 'BLOB', 'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET', 'MSE', 'PHE', 'PRO', 'SEC', 'SER', 'THR', 'TRP', 'TYR', 'VAL', 'DA', 'DG', 'DT', 'DC', 'DU', 'A', 'G', 'T', 'C', 'U', 'HOH', 'H20', 'WAT')) W zbiorze danych występuje kolumna, która równa jest NA we wszystkich wierszach.
rows_without_na_in_weight_co = filter(cleaned_data, !is.na(weight_col))
dim(rows_without_na_in_weight_co)[1]## [1] 0Jak widać brak wierszy, których kolumna weight_col nie ma wartości pustej, dlatego możemy ją wykluczyć z dalszej analizy.
cleaned_data_without_empty_col <- select(cleaned_data, -weight_col)Rozmiar danych oraz ich statystyki
Zebrane dane zawierają… 408 kolumn oraz 1000 wierszy. Dane są typu character, integer, numeric. Większość kolumn jest numeryczna. Ich podstawowe statystyki prezentują się tak:
Natomiast pozostałe kolumny są następujące
knitr::kable(summary(cleaned_data_without_empty_col %>% select_if(is.character)))| pdb_code | res_name | chain_id | skeleton_data | fo_col | fc_col | |
|---|---|---|---|---|---|---|
| Length:1000 | Length:1000 | Length:1000 | Length:1000 | Length:1000 | Length:1000 | |
| Class :character | Class :character | Class :character | Class :character | Class :character | Class :character | |
| Mode :character | Mode :character | Mode :character | Mode :character | Mode :character | Mode :character |
Analiza
Ograniczenie danych
Naszą analizę ograniczymy do 50 najpopularniejszych wartości kolumny res_name
top_50_res_name <- cleaned_data %>%
select(res_name) %>%
group_by(res_name) %>%
count() %>%
arrange(desc(n)) %>%
head(50)
data_with_most_common_res_names <- cleaned_data %>% filter(res_name %in% top_50_res_name$res_name)Rozkład jej wartości prezentuje się następująco
Korelacja
W celu sprawdzenia korelacji użyjemy korelacji Rho Spearmana, ponieważ rozkład wartości przynajmniej jednej kolumny nie jest rozkładem normalnym
correlation <- as.data.frame(as.table(cor(data_with_most_common_res_names %>% select_if(is.numeric), use="complete.obs", method="spearman")))## Warning in cor(data_with_most_common_res_names %>% select_if(is.numeric), :
## odchylenie standardowe wynosi zeroUsuniemy teraz korelacje kolumn samych ze sobą
correlation <- correlation %>%
rename(first_column = Var1, second_column = Var2, freq = Freq) %>%
filter(first_column != second_column)Grupujemy po pierwszej kolumnie oraz dla każdej wartości obliczamy maksymalną wartośi. Następnie wyznaczmy 10 kolumn z największą korelacją oraz filtrujemy dane do wizualizacji
top_correlated <- correlation %>%
group_by(first_column) %>%
summarise(max=max(freq, na.rm = TRUE)) %>%
arrange(desc(max)) %>%
head(10)
correlation <- correlation %>% filter((first_column %in% top_correlated$first_column & second_column %in% top_correlated$first_column))
Rozkład wartości atomów oraz eleketronów
Niezgodność liczby atomów
Niezgodność liczby elektronów
Rozkład wartości kolumn rozpoczynających się od “part_01”
part_01_all <- data_with_most_common_res_names %>% select(contains('part_01'))
part_01_all <- gather(part_01_all, 'key', 'value')
n_pages <- ceiling(
length(levels(factor(part_01_all$key))) / 9
)
continuous_means <- part_01_all %>% group_by(key) %>% summarise(mean_value=mean(value))
for (i in seq_len(n_pages)) {
print(ggplot(part_01_all, aes(value, fill=1)) +
geom_density(show.legend=FALSE) +
geom_vline(data=continuous_means, aes(xintercept=mean_value), linetype="dashed") +
geom_text(data = continuous_means, aes(label = mean_value, y=1, x=mean_value)) +
facet_wrap_paginate(~ key, ncol = 3, nrow = 3, scales='free', page = i) +
theme_minimal())
}## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
## Warning: Removed 1152 rows containing non-finite values (stat_density).## Warning: Removed 96 rows containing missing values (geom_vline).## Warning: Removed 96 rows containing missing values (geom_text).
Animacja
animation_data <- data_with_most_common_res_names %>%
select(res_name,
part_00_shape_segments_count, part_00_density_segments_count,
part_01_shape_segments_count, part_01_density_segments_count,
part_02_shape_segments_count, part_02_density_segments_count)
animation_data <- gather(animation_data, 'key', 'value', -res_name)
animation_data <- animation_data %>% separate(key, into = c("name", "part_number", "abc")) %>% mutate(part_number=as.integer(part_number) + 1)## Warning: Expected 3 pieces. Additional pieces discarded in 4920 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].p <- ggplot(animation_data, aes(abc, value, colour=res_name)) +
geom_point(position="jitter") +
transition_time(part_number) +
theme_bw() +
labs(title="{round(frame_time)}")
animate(p, nframes = 150, fps = 30, width = 600, height = 600,
renderer = gifski_renderer(loop = F))
Regresja
Wyznaczanie liczby elektronĂłw
correlation <- cor(data_with_most_common_res_names %>% select_if(is.numeric), use="complete.obs", method="spearman")## Warning in cor(data_with_most_common_res_names %>% select_if(is.numeric), :
## odchylenie standardowe wynosi zerocorrelation <- as.data.frame((correlation))
correlationelectron_count_predict_data <- select(data_with_most_common_res_names, (local_res_atom_non_h_electron_sum:local_res_atom_S_count))
electron_count_predict_dataindexes <- createDataPartition(electron_count_predict_data$local_res_atom_non_h_electron_sum,
p=0.7, list=F)
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## [576,] 819training_data <- electron_count_predict_data[indexes,]
testing_data <- electron_count_predict_data[-indexes,]
training_datatesting_datactrl <- trainControl(
method = "repeatedcv",
number = 2,
repeats = 5)
fit <- train(local_res_atom_non_h_electron_sum ~ .,
data = training_data,
method = "lm",
trControl = ctrl)
fit## Linear Regression
##
## 576 samples
## 5 predictor
##
## No pre-processing
## Resampling: Cross-Validated (2 fold, repeated 5 times)
## Summary of sample sizes: 288, 288, 289, 287, 288, 288, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 16.70894 0.9717743 11.17189
##
## Tuning parameter 'intercept' was held constant at a value of TRUEpredicted_values <- predict(fit, newdata = testing_data)
predicted_values## 4 5 9 12 13 15 16
## 98.21186 98.21186 55.98714 55.98714 55.98714 55.98714 55.98714
## 22 24 25 27 30 38 44
## 55.98714 53.05014 53.05014 53.05014 55.98714 50.96603 337.64707
## 46 55 57 60 61 62 63
## 150.83809 223.48021 38.20415 38.20415 38.20415 223.48021 18.30966
## 65 67 69 70 71 73 78
## 38.20415 38.13889 37.38832 53.09909 15.12790 55.98714 38.20415
## 82 83 84 87 89 92 93
## 42.96658 42.96658 42.96658 55.98714 18.79916 200.00072 42.96658
## 96 98 102 103 108 111 114
## 55.98714 50.96603 378.13846 105.07187 15.73978 105.07187 18.79916
## 119 123 126 133 139 142 144
## 50.96603 38.20415 38.20415 337.64707 290.55235 290.55235 290.55235
## 153 158 159 160 162 166 170
## 290.55235 290.55235 290.55235 290.55235 290.55235 290.55235 16.55561
## 176 182 186 189 196 199 202
## 290.55235 290.55235 290.55235 290.55235 290.55235 290.55235 88.56163
## 208 218 219 223 224 226 237
## 88.56163 88.56163 88.56163 88.56163 88.56163 88.56163 187.94058
## 238 241 242 245 248 250 251
## 187.94058 187.94058 50.96603 50.96603 50.96603 50.96603 18.79916
## 258 259 260 262 266 271 275
## 17.98332 17.98332 17.98332 14.61801 38.17355 50.96603 55.98714
## 276 279 280 282 286 290 291
## 53.53964 53.53964 53.53964 53.53964 53.53964 233.77025 233.77025
## 294 295 296 299 300 312 313
## 233.77025 233.77025 233.77025 233.77025 233.77025 15.12790 15.12790
## 315 317 319 320 327 330 337
## 15.12790 15.12790 15.12790 14.31207 15.12790 14.31207 50.96603
## 344 346 355 356 358 361 368
## 105.07187 105.07187 105.07187 105.07187 105.07187 105.07187 13.93678
## 374 376 390 393 394 395 408
## 82.01466 82.01466 16.75957 96.05836 96.05836 96.05836 15.12790
## 410 417 420 421 431 432 434
## 56.92121 290.55235 290.55235 290.55235 55.98714 55.98714 38.17355
## 438 442 447 449 454 456 458
## 230.28691 42.96658 314.16758 16.14770 378.13846 50.96603 15.73978
## 459 461 462 466 467 471 476
## 50.96603 50.96603 55.98714 38.20415 18.79916 38.20415 89.25166
## 478 481 495 496 502 503 504
## 186.90827 55.98714 18.79916 18.79916 18.79916 16.75957 16.75957
## 506 507 513 521 526 530 532
## 204.00123 204.00123 223.69770 38.20415 16.55561 290.55235 290.55235
## 542 546 547 549 550 552 555
## 290.55235 290.55235 290.55235 290.55235 290.55235 105.07187 105.07187
## 556 562 563 565 566 571 572
## 105.07187 105.07187 105.07187 105.07187 55.98714 55.98714 55.98714
## 573 574 577 579 583 587 592
## 55.98714 55.98714 55.98714 55.98714 55.98714 105.07187 55.98714
## 594 596 600 605 606 612 620
## 55.98714 55.98714 96.05836 47.47739 16.55561 16.55561 50.96603
## 628 631 635 637 640 641 644
## 17.77937 16.75957 50.96603 50.96603 50.96603 50.96603 16.75957
## 646 657 664 669 672 673 674
## 55.98714 94.35531 94.35531 305.11182 241.60232 241.60232 241.60232
## 677 682 684 685 686 692 694
## 241.60232 241.60232 16.55561 105.07187 105.07187 56.92121 105.07187
## 696 697 701 702 709 710 711
## 56.92121 16.14770 105.07187 56.92121 84.14251 60.32181 66.27698
## 713 715 717 718 723 725 728
## 66.27698 16.14770 60.32181 36.50110 16.14770 60.32181 72.23216
## 729 731 732 736 739 743 746
## 96.05286 15.98453 16.75957 14.33246 24.10209 24.10209 16.55561
## 756 757 760 765 767 771 772
## 48.73880 38.20415 327.12194 76.48979 76.48979 50.96603 14.56498
## 780 783 784 794 796 799 801
## 21.49141 17.57541 22.47042 187.45726 20.51241 176.52124 191.63842
## 807 808 814 815 818 820
## 18.79916 18.79916 50.96603 50.96603 38.20415 55.98714RMSE(testing_data$local_res_atom_non_h_electron_sum, predicted_values)## [1] 17.49885R2(testing_data$local_res_atom_non_h_electron_sum, predicted_values)## [1] 0.9691841